ライフサイエンスコーパス: inference

1 criptomics (transcriptome-based phylogenetic inference).

2 importance sampling scheme to perform model inference.

3 re often not informative enough for reliable inference.

4 on probability, as required by Bayes-optimal inference.

5 e topology and clade support in phylogenomic inference.

6 the effects of recombination on phylogenetic inference.

7 is likely to improve the quality of ortholog inference.

8 ain regions involved in sophisticated social inference.

9 ucture prediction tools and protein function inference.

10 e the same source, a process known as causal inference.

11 xamine off-target effects and furnish causal inference.

12 oblems in metagenome profiling and cell type inference.

13 d that these regions were involved in social inference.

14 egate data, which precludes individual-level inference.

15 h regional-scale persistence and cross-scale inference.

16 be considered as a process of probabilistic inference.

17 using permutation testing and cluster-based inference.

18 ctures without convolution layers in protein inference.

19 onal cost and facilitate model selection and inference.

20 B, and Lasso-PT fail to be viable methods of inference.

21 genomic datasets can be integrated into the inference.

22 ratio are computed, and used for statistical inference.

23 on previous implementations of probabilistic inference.

24 protein interactions) for biological network inference.

25 ds while offering the advantage of posterior inference.

26 disease-associated variants, and test causal inference.

27 y mimics the computational units of Bayesian inference.

28 ting that dominates statistical analysis and inference.

29 oltz characterized perception as unconscious inference.

30 ation to the exact finite sample conditional inference.

31 es, the tribe Paniceae, to make phylogenomic inferences.

32 emically characterized before making in vivo inferences.

33 researchers, with the potential for spurious inferences.

34 easurements and perturbations support causal inferences.

35 ses and other psychosocial factors affecting inferences.

36 rol, and retrospective 'sense-making' causal inferences.

37 rce," "work," or "effort"-supported infants' inferences.

38 of data filtering influence phylogeographic inferences.

39 annotated and allow more specific functional inferences.

40 ingle marker at a time, thereby limiting our inference about a complete picture of the genetic archit

41 nd leverage environmental monitoring to make inference about infectivity.

42 nd uncertain sensitivity and specificity, so inference about the cause is complex.

43 comparison groups that inevitably complicate inference about the role of deployment itself.

44 terior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of

45 mple training, and that they could then make inferences about category order.

46 e of complexity and can significantly impact inferences about general abilities in sensory perception

47 Here, we test the ability to make inferences about neural tuning width from inverted encod

48 veillance data can have a dramatic impact on inferences about population processes, where the failure

49 often use spatially aggregated data to draw inferences about population trends and drivers, potentia

50 important to use multiple tasks when making inferences about sensory or cognitive processing.

51 rast, acceptability judgments enable clearer inferences about structure.

52 nd we argue that the former does not warrant inferences about the nature or evolution of the latter.

53 ine or placebo, and it supported true causal inferences about treatment effects on the brain by contr

54 ported software packages, mainly focusing on inference accuracy and computational resources used.

55 orithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement).

56 we show that our model with a variational EM inference algorithm has higher specificity than many sta

57 eneral probabilistic model and an associated inference algorithm that unify the model-based and data-

58 Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory

59 , next-generation sequencing, and a Bayesian inference algorithm to rapidly process and then accurate

60 We also develop a scalable sampling-based inference algorithm using a latent variable representati

61 h a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficie

62 s gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical mo

63 ictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3.

64 On the basis of three existing phylogenetic inference algorithms, we built an integrated pipeline fo

65 rthoFinder and HaMStR, two common orthogroup inference algorithms.

66 rnative methods for statistical analysis and inference, all other strategies for improving reproducib

67 Meta-analytic reverse-inference analysis showed that these regions were involv

68 lly, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accu

69 cations of this methodological variation for inference and comparability among studies have not been

70 Phylogenetic inference and different gene expression patterns support

71 rove the accuracy of gene regulatory network inference and facilitate candidate selection for experim

72 We compare statistical inference and forecasts from our hierarchical Bayesian m

73 Demographic inference and modeling of the evolution of rare variants

74 ucture, much more can be done to improve our inference and network analysis.

75 ing provides a framework to make statistical inference and probabilistic forecasts, using mechanistic

76 (G or C) have insufficient read evidence for inference and therefore could not be assayed precisely b

77 tions on white matter tracts for statistical inference and to study the white matter geometrical orga

78 olution, but it does not incorporate network inference and underperforms in eukaryotes.

79 g methods allow for portrayal of demographic inferences and highlight genetic variation indicative of

80 ain Monte Carlo techniques to provide robust inferences and quantify the uncertainty in our estimates

81 ry experiments, clinical data, computational inference, and mechanistic computational models.

82 scopy, biophysical measurements, statistical inference, and molecular simulations, we provide a quant

83 model in the literature, employing MCMC for inference, and obtain comparable results with a small fr

84 he key components of cue combination, causal inference, and temporal integration, which highlights th

85 he most informative perturbation for network inference, and, identifies core TFs whose targets are pr

86 ly the STELLS2 algorithm in the species tree inference approach in the original STELLS, which leads t

87 Using a network inference approach we predict the regulatory relationshi

88 elationships enhances the utility of network inference approaches in non-model species where experime

89 More recently, methodologies of causal inference are being applied to maximize the information

90 These inferences are based on distributions of thaumarchaeote

91 Using computations, we show that discrepant inferences are neither due to methodological shortcoming

92 sal models and provide explanations of their inferences are not new, and advocate a cognitive functio

93 vity, and oppositional behaviors, but causal inferences are precluded by the correlational nature of

94 TELLS2 is almost as accurate in species tree inference as STELLS.

95 Such inferences assume that individual delta(34)S records ref

96 Both the coefficient-based ranking and the inference based on the model lead to a plausible priorit

97 n should be exercised when making biological inferences based on these reported eQTL.

98 ization is unnecessary and misleading, as in inferences based on whether a P value is "statistically

99 ast software Findr for higly accurate causal inference between gene expression traits using cis-regul

100 not only solve problems of optimization and inference but also to implement precise Boolean function

101 can further improve the quality of activity inference by imposing a constraint on the minimum spike

102 percutaneous drainage, and although no firm inference can be made from such a small series, we have

103 sional nature of microbial data, statistical inference cannot offer reliable results.

104 Limitations of this study are that inferences cannot be drawn about the role of circulating

105 The theoretical basis for this inference comes from previous models that assume only th

106 ew evidence that an additional system guides inference concerning the hidden states of other agents,

107 These inferences conflict with conclusions of a high and dry T

108 Species tree inference consistently returned the same phylogeny, but

109 theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differentia

110 how conceptualizing perception and action as inference-control loops yields a joint computational per

111 and colonization by flowering plants and, by inference, could have been a major contributor to this p

112 We demonstrated the prospects of combining inferences derived from two unique analytical methods to

113 These inferences do not require energy minimization algorithms

114 of studies have reported inconsistencies in inferences drawn from the two sets of measurements for t

115 Our results suggest that probabilistic inference emerges naturally in generic neural networks t

116 social inference - minimal (TASIT2), social inference - enriched (TASIT3), and the RAD tests.

117 forming such accurate and efficient Bayesian inference for enzyme kinetics is provided.

118 the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear

119 -intensive applications such as phylogenetic inference for large-scale sequences.

120 ed the most among habitats, which could have inferences for as much as half of all reef fishes which

121 event of the Cenozoic-is central to drawing inferences for future climate change.

122 The coupled model-inference framework is then used to generate retrospecti

123 ucture, and develop a nonparametric Bayesian inference framework that identifies the simplest such mo

124 In addition, we applied a causal inference framework to estimate the potential reduction

125 The uncertainty associated with inference from a small sample of in-camp households and

126 arly human development is typically based on inference from animal models, which may not fully recapi

127 However, 3D structural inference from high-resolution Hi-C datasets is often co

128 at rely on direct observation, by its use of inference from indirect evidence.

129 ry, this work demonstrates that evolutionary inference from integrated genomic analysis in multisecto

130 quantitative modeling when making a reverse inference from population response profiles to single-un

131 Probabilistic inference from real-time input data is becoming increasi

132 Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundati

133 However, current methods for TCR inference from scRNA-seq are limited in their sensitivit

134 tegy for personalized medicine that enhances inference from static genotypic risk assessment.

135 he course of human evolution, but behavioral inference from the fossil record is hampered by a lack o

136 oal was to evaluate the potential for causal inference from the studies.

137 Making accurate inferences from chromatin profiling experiments that inv

138 enges and algorithms associated with drawing inferences from DNA methylation data, including cell-typ

139 sal distance and the ability of SNPs to make inferences from single individuals.

140 dering spatially structured dynamics, as the inferences from such an approach can lead to a different

141 el for vision in which message-passing-based inference handles recognition, segmentation, and reasoni

142 ting software application for local ancestry inference, HAPMIX.

143 Human perceptual inference has been fruitfully characterized as a normati

144 e more realistic models, no formal parameter inference has previously been conducted and the expressi

145 ons, but the assumptions necessary for valid inference have only partially been articulated.

146 utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks):

147 This procedure can improve inference if only a particular class of variants confers

148 ons required to draw internally valid causal inference in a specific study sample.

149 Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks.

150 European ancestry (LEA) using Local Ancestry inference in adMixed Populations using Linkage Disequili

151 Meaningful inference in epidemiology relies on accurate exposure me

152 stimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constr

153 knowledge, the first evidence for sequential inference in human cognition, and by exploiting between-

154 We describe a simplified model of causal inference in multisensory speech perception (CIMS) that

155 ning rule perform near-optimal probabilistic inference in nine common psychophysical tasks.

156 ion of a more circumspect approach to causal inference in the neuroscience of stress.

157 to assess the accuracy of recombination rate inference in the presence of phase errors, and we used a

158 ent changes in sensory responses, perceptual inference in the presence of signal-dependent noise acco

159 nclusions have far-reaching implications for inferences in leaf hydraulics, gas exchange, water use,

160 Perception can be described as a process of inference, integrating bottom-up sensory inputs and top-

161 Accurate orthology inference is a fundamental step in many phylogenetics an

162 Expression-based network inference is among the most popular methods to infer reg

163 Phylogenetic inference is an attractive means to reconstruct transmis

164 Because phylogenetic inference is an important basis for answering many evolu

165 perform sophisticated other-regarding social inference is associated with the structural changes of s

166 As a result, tree inference is fast, accurate and robust to noise.

167 This inference is supported by gene expression profiles highl

168 e cross-sectional data does not allow causal inference it could also be that individuals with high st

169 ing LD and minor allele frequency stratified inference (LDMS).

170 ding Hulleman & Olivers' [H&O's]) comes from inferences made using changes in mean RT as a function o

171 rial and two nuclear markers, using Bayesian Inference, Maximum Likelihood, genetic divergence, molec

172 hat it relies on case surveillance, and thus inference may be biased by age-specific variation in mea

173 ecies and their outcrossing relatives, where inferences may be confounded by secondary mutations that

174 or inaccurate claims but also to assess what inferences may or may not be drawn about informants give

175 analyses using multiple, alternative causal inference measures and simulation studies demonstrated c

176 ads to a new maximum likelihood species tree inference method (also called STELLS2).

177 We propose a statistical inference method for tumor phylogenies from noisy single

178 pling analysis (DCA), a powerful statistical inference method that has been successfully applied to p

179 ility, we formulate a generalized mechanical inference method to obtain the spatiotemporal distributi

180 Here we developed and applied a network inference method, exploiting the ability to infer dynami

181 ynamic regime, which we reveal using a novel inference method.

182 It has recently been demonstrated that inference methods based on genealogical processes with r

183 Improving the accuracy of computational inference methods can significantly reduce the cost and

184 Existing probabilistic inference methods for such models rely on subjective fil

185 Recently, several fast species tree inference methods have been developed, which can handle

186 ng (RADseq), in combination with demographic inference methods, are improving our ability to gain ins

187 the scalability and robustness of orthology inference methods.

188 mance on emotion evaluation (TASIT1), social inference - minimal (TASIT2), social inference - enriche

189 We outline a Bayesian inference model, incorporating the key components of cue

190 , we constructed a series of gene expression inference models based on genes common to both platforms

191 f states at neighboring sites and allows for inference of ancestral states.

192 somatic DNA mutations in tissues permitting inference of clonal relationships.

193 te models, such cross-talk prevents accurate inference of concentrations of individual ligands.

194 This is a step towards real-time inference of engagement in the classroom.

195 Bayesian inference of evolutionary rates shows that genotypes 3 a

196 10 plant species, thus allowing genome-wide inference of gene function.

197 Inference of genome-wide regulatory networks is central

198 ng TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profi

199 current methods and further facilitates the inference of histories of complex population admixtures.

200 The etiologic inference of identifying a pathogen in the upper respira

201 quenched and unquenched chromophores allowed inference of multiple conformations.

202 We also demonstrated the inference of networks and the evaluation of association

203 anonical secondary structure, allow accurate inference of non-canonical pairs, an important step towa

204 UCTURE analysis runs required for downstream inference of optimal K.

205 Here, we present inference of peptidoforms (IPF), a fully automated algor

206 e or multiple exponential functions, for the inference of recent single- or multiple-wave admixture.

207 terative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues

208 here a statistical framework for the precise inference of structural alignments, built on the Bayesia

209 on and a corollary, permitting retrospective inference of the distribution of fitness and activity in

210 nstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem c

211 A sequencing, and suitable for computational inference of the expression levels of 81% of non-measure

212 der basal and activated conditions, enabling inference of transcription factor networks that direct h

213 This means that inferences of statistical patterns of language in acoust

214 ch developmental effects can severely affect inferences of trees' W i .

215 ased on stochastic block models and Bayesian inference) of each articulation.

216 he localization of receptor protein, and, by inference, of functional receptors, has been limited by

217 How humans reliably perform such inferences, often in the face of radically incomplete in

218 formulation leads to a general framework for inference on changes in brain network structures across

219 The sum constraint makes inference on correlations between unconstrained features

220 e data on influenza virus activity permitted inference on influenza-associated hospitalizations and d

221 en up the ability to perform divergence time inference on large phylogenetic studies.

222 However, it is unclear how to best conduct inference on the individual Lasso coefficients, especial

223 among the sources and in human cases allows inference on the likely source of each infection.

224 Finally, we applied our ABC inference on two different outbreaks from the Swedish HI

225 tion mechanism of myosin VI, allowing direct inferences on myosin VI function.

226 tions are significant, with key sex-specific inferences on physical function, frailty, disability, an

227 ls with and without spatial structure affect inferences on population trends and the identification o

228 a structural framework for making functional inferences on RNA.

229 process of a decision rather than a post hoc inference or arbitrary report.

230 ic behavior of the Loma Blanca fault and, by inference, other intraplate faults.

231 As our approach relies on Bayesian inference our scheme transcends individual sequence anal

232 on the unobserved processes in order to draw inferences, our Bayesian approach includes the unobserve

233 the statistical benefits of performing joint inference over multiple participants and the value of us

234 acaques can learn categories by a transitive inference paradigm in which novel exemplars of five cate

235 ning and broaden the scope of the transitive inference paradigm.SIGNIFICANCE STATEMENT The cognitive

236 Our multimodel inference phylogenetic approach showed that the barriers

237 esearch: how to address challenges to causal inference posed by wealth's cumulative nature and how to

238 computation along with simple procedures for inference, prediction and goodness-of-fit assessments.

239 ods hold some promise for ecological network inference, presence-absence data does not provide enough

240 t at the same time pose a hard computational inference problem.

241 We present a rapid and powerful inference procedure for identifying loci associated with

242 In intuitive psychology, many inferences proceed without detailed causal generative mo

243 ic regression provides a concise statistical inference process and reduces spurious associations from

244 As a result of the inference process, we obtain a matrix of values correspo

245 ve functions, the neural mechanisms of these inference processes remain to be elucidated.

246 hat spatial aggregation leads to more robust inference regarding dynamics.

247 As a result, the strength of inductive inference regarding linguistic representation is rather

248 efficiencies, the learning process provides inferences regarding patterns that underlie the mechanis

249 ns in the gas phase, as well as more general inferences regarding the sensitivity of collision induce

250 ses equally contribute to the likelihood for inference, regardless of their onset age.

251 nt MBG3 models are currently lacking, making inferences related to their cellular origin thus far lim

252 number of species, generality of mechanistic inferences remains to be tested in tissue culture system

253 Coalescent-based inference required fewer sampled individuals (i.e., n =

254 Probabilistic inference requires trial-to-trial representation of the

255 formats as well as standardize and summarize inference results for four popular local ancestry infere

256 Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and

257 Using machine learning tools, we describe an inference scheme using the currently available inflammat

258 in an Approximate Bayesian Computation (ABC) inference scheme, and suggest that parameters simulated

259 orating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activ

260 tive reliability, as predicted by a Bayesian inference scheme.

261 However, large-scale inference sets a high bar for both statistical power and

262 Photochemical models corroborate this inference, showing Delta(36)S/Delta(33)S ratios are sens

263 ial, temporal prediction and Bayesian causal inference.SIGNIFICANCE STATEMENT Looming stimuli have a

264 s convenience to run multiple local ancestry inference software.

265 ence results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI.

266 gorithm: (i) refining the pairwise orthology inference step to account for same-species paralogs evol

267 subject measures of this implicit sequential inference strategy had a neurobiological underpinning an

268 ore concrete conceptual framework to clarify inference surrounding risk effects and their cascading e

269 ompared the models with Adaptive Neuro-Fuzzy Inference System [ANFIS], a method previously unused in

270 s, demixing is fundamentally a probabilistic inference task.

271 ive approaches and the application of causal inference techniques in epigenetic epidemiology.

272 he mind in the eyes, the awareness of social inference test (TASIT) parts 1, 2, and 3, and the relati

273 Using causal inference testing, we searched causal variants across ei

274 AD-LIBS is an effective tool for ancestry inference that can be used even when few individuals are

275 he context of current notions about Bayesian inference that find their historical roots in von Helmho

276 ards to fMRI technology: how the BOLD signal inferences the underlying microscopic neuronal activity

277 In addition to improving inference, the cross-scale quantitative antibody approac

278 Protein inference, the identification of the protein set that is

279 to primitive, chondritic meteorites and, by inference, the primordial disk from which they formed.

280 Using network inference, the transcription factor ATHB5 was identified

281 riginal evidence that uterine glands and, by inference, their secretions play important roles in blas

282 al principles and simulations, we use active inference to demonstrate how attention and salience can

283 ons to BioMagResBank, and tools for Bayesian inference to enhance the robustness and extensibility of

284 Here we use statistical methods for causal inference to investigate the drivers of marine invertebr

285 The CIMS model uses causal inference to provide a computational framework for study

286 y olfactory system uses approximate Bayesian inference to solve it.

287 analysts to assess the sensitivity of their inferences to different assumptions about the extent of

288 fety, we suggest transitioning their QC from inference- to verification-based practice by developing

289 We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized

290 within this framework, utilizing statistical inference tools to quantify the fitness effects of segre

291 Phylogenetic inference typically invokes nocturnality as ancestral in

292 tationally intensive methods of phylogenetic inference using (for example) maximal likelihood methods

293 Overall, our results reveal that demographic inference using RADseq data can be successfully applied

294 veloped SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for th

295 re flexible framework that results in better inference when proper prior knowledge exists.

296 d neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect t

297 be described as phylogenomics (phylogenetic inference with entire genomes).

298 A common approach to statistical inference with stochastic dynamic models relies on produ

299 Comparative inferences with other East African and western Asia fat-

300 riables is a core concept of cell biological inference, with co-localization of two molecules as a pr